The demand for solar electric power has increased and this increased demand has resulted not only in more utility-scale solar photovoltaic (PV) power plants becoming operational but also a larger number of PV arrays being used at these PV power plants to meet the increased demand. Hence, because of the larger PV arrays being used, there is a multiple factor uptick in the use of solar panels which make up the PV arrays used in the solar electric power generation. Therefore, these large solar farms that have become operational and generating greater amounts of electrical power is in fact due to voluminous numbers of individual solar panels each contributing to the overall power generations by the PV arrays of these plants.
Initially, during the manufacturing process, PV panels need to be inspected for defects such as cracks and hot spots. After PV arrays are installed in a utility-scale solar plant and operational periodic inspection are still needed to identify defects. It is important that all the panels in an array are working properly together to generate the maximum amount of electricity. That is, power output decreases when individual cells or panels malfunction due to a variety of reasons such as defects in manufacturing, age, or operational damage. These malfunctioning panel usually emit more heat as compared to a normally functioning panel because it converts less luminous energy directed at the panel into electrical energy resulting in thermal radiation to dissipate causing the resultant heat. Hence, efficient operation of solar panels requires the panels to operate failure free and affects the overall efficiency of commercial solar plants where commercial feasibility and profitability requires the plants to operate at high efficiencies.
To achieve higher power generation efficiency and longer panel life, a simple and reliable panel evaluation and defect ascertainment method and system is therefore desirable. Thermal infrared imaging combined with image processing techniques provides an approach to achieving this desired goal rather than the use of conventional electrical detection circuitry and other similar prior approaches which all suffer numerous drawbacks which include being expensive, time-consuming and inefficient in implementation. Accordingly, improved systems and methods for using IR imaging for solar panel and solar panel array assessment and defect detection are desirable.
Accordingly, it is desirable to have improved solutions for detecting defects in solar panels using IR imaging for detecting defects in groups of solar panels of the PV arrays and individual solar panels. In addition, it is desirable to use image processing solutions in conjunction with IR image capturing to provide real-time processing for panel recognition and defect detection of defects in solar panel arrays and individual solar panels.
In addition, it is desirable to use image processing techniques of the captured IR images that require limited prior information to perform associated processes used in the image processing of filtering and segmentation of the captured images for the panel recognition and the subsequent defect detection of the defects in the plurality of solar panels of the PV arrays and individual solar panels.
It is also desirable to detect defects by automatically capturing IR images or streams or imagery by an IR camera mounted on different vehicles such vehicles conventionally used for inspections of panels in solar farms, for vehicles that may have autonomous capabilities for navigating the solar farms and for remote drones where in each case the mounted camera may provide real-time information to a user locally or remotely via cloud based applications for inspections by inspectors at the site or remote sites or other locations for convenience and efficiency. In addition, it is desirable to utilize machine learning and artificial intelligence applications to aid in identifying and locating detected defects without user involvement adding to the automation and efficiency of the defect detection process.
It is desirable to use data analysis to identify in real-time local hot regions and related defects in individual panels as well as thermal IR differences among panels in pluralities of panels in PV arrays for entire panel failures using algorithmic solutions related to clustering of the data received, comparisons of pixels within IR images of individual panels and comparisons of mean IR thresholds values of entire panels with other thermal IR panel values in a PV array.
It is desirable to generate identification information of each panel captured by IR imagery and to track and locate the individual panels using the generated identification information in a PV array in association with the defect detection identification identified with the individual panel.
It is desirable to not require training of panel assessment systems and to adjust in real-time for adverse effects that may affect the panel recognition and defect detection including missing rows of panels in IR images captured of the PV array, failures in initialization of counters associated with panel identification and failures in image registration.
Furthermore, other desirable features and characteristics of the present disclosure will become apparent from the subsequent detailed description of the invention and the appended claims, taken in conjunction with the accompanying drawings and this background of the invention.